Privacy-sensitive bayesian network parameter learning (2004)
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| Venue: | In Proc. of the Fourth IEEE International Conference on Data Mining |
| Citations: | 5 - 1 self |
BibTeX
@INPROCEEDINGS{Meng04privacy-sensitivebayesian,
author = {D. Meng and K. Sivakumar},
title = {Privacy-sensitive bayesian network parameter learning},
booktitle = {In Proc. of the Fourth IEEE International Conference on Data Mining},
year = {2004},
pages = {487--490}
}
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Abstract
This paper considers the problem of learning the parameters of a Bayesian Network, assuming the structure of the network is given, from a privacy-sensitive dataset that is distributed between multiple parties. For a binary-valued dataset, we show that the count information required to estimate the conditional probabilities in a Bayesian network can be obtained as a solution to a set of linear equations involving some inner product between the relevant different feature vectors. We consider a random projection-based method that was proposed elsewhere to securely compute the inner product (with a modified implementation of that method). 1.
Citations
| 483 | Privacy-Preserving Data Mining - Agrawal, Srikant - 2000 |
| 198 | Limiting Privacy Breaches in Privacy Preserving Data Mining - Evfimievski, Gehrke, et al. - 2003 |
| 89 | Tools for privacy preserving distributed data mining - Cliffton, Kantarcioglu, et al. - 2004 |
| 80 | Secure multiparty computation of approximations - Feigenbaum, Ishai, et al. - 2001 |
| 45 | S.: Privacy-preserving multivariate statistical analysis: Linear regression and classification - Du, Han, et al. - 2004 |
